Combining diverse databases to get a sufficient accuracy in all sub-populations is even more critical for high-stakes systems, like those commonly used in Medicine.
The fact that medical cohorts and longitudinal databases suffer from biases has been long ago acknowledged in medical studies.
The situation is even more complex in medical image analysis for specialties such as radiology (National Lung Screening Trial, MIMIC-CXR-JPG [1]}, CheXpert [2]}) or dermatology (Melanoma detection for skin cancer, HAM10000 database [3]}), where biased datasets are provided for medical applications.
Indeed, under-represented populations in some datasets lead to critical drop of accuracy, for instance in skin cancer detection as in [4]}, [5]} or for general research in medicine [6]} and references therein.
